The ability to gather data from a large number of vehicles on the road makes a new level of analytics possible. Interested parties can gather traffic and weather, performance, road characteristics and all other sorts of data, aggregate the data and perform wide-scope analytics. Prior to the existence of telematics units, allowing on-demand and/or regular reporting, gathering this scale of data was a difficult task. Now, however, many vehicles can report data upon request, and with enough data predictions about many systems can be formulated and refined.
By observing various situations and driving states, many aggregated observed actualities can be aggregated to form a reasonable prediction about similar conditions under which a similar instance will again occur. For example, if 100,000 electric vehicles report a 0.2% drop in energy efficiency when temperature drops below 20 degrees, this data would be useful to determine that there is likely a cause-effect occurring, and the data would also be useful to other vehicles attempting to predict distance to empty under operating conditions below 20 degrees.
There is a wide variety of utility in crowdsourced data, and the illustrative embodiments demonstrate some examples of a useful concept achievable under such a model.